The normative appeal of GGFs lies in their ability to address a multiplicity of fairness criteria studied in the fair recommendation literature. Since GGFs include the well-known Gini inequality index as a special case [1]}, they can be used to optimize trade-offs between exposure inequality among items and user utility, a goal seeked by many authors [2]}, [3]}. GGFs also conveniently specify normative criteria based on utility quantiles [4]}: for instance, it is possible to improve the utility of the \(10\%\)  worse-off users and/or items with GGFs, simply by assigning them more weight in the objective. Moreover, using techniques from convex multi-objective optimization, we show that GGFs cover all ranking policies that satisfy Lorenz efficiency, a distributive justice criterion which was recently introduced for two-sided fairness in rankings [4]}.
